scholarly journals DEVELOPMENT OF OPERATIONAL PREDICTION METHOD OF FOREST FIRE DYNAMICS BASED ON ARTIFICIAL INTELLIGENCE AND DEEP MACHINE LEARNING

2018 ◽  
Vol 22 (9) ◽  
pp. 111-120 ◽  
Author(s):  
Tatiana S. Stankevich ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Forest Fire ◽  
Prediction Method ◽  
Fire Dynamics

scholarly journals SIMULATION OF SPREADING FOREST FIRE UNDER NONSTATIONARITY AND UNCERTAINTY BY MEANS OF ARTIFICIAL INTELLIGENCE AND DEEP MACHINE LEARNING

2019 ◽  
pp. 97-107
Author(s):  
Tatiana Sergeevna Stankevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Forest Fire ◽  
Noise Removal ◽  
Fire Dynamics ◽  
Operational Forecast ◽  
Forecasting Models ◽  
Artificial Neural

The article describes the results of increasing the efficiency of operational forecast of the forest fire dynamics under nonstationarity and uncertainty through the fire dynamics modeling based on artificial intelligence and deep machine learning. To achieve the goal there were used following methods: system analysis method, theory of neural networks, deep machine learning method, method of operational forecasting of the forest fire dynamics, method of filtering images (modified median filter), MoSCoW method, and ER-method. In the course of study there have been developed forest fire forecasting models (models of treetop and ground fires) using artificial neural networks. The developed models solve the recognition and forecasting problems in order to determine the dynamics of forest fires in successive images and generating images with a forecast of fire spread. There has been given the general logical scheme of the proposed forest fire forecasting models involving five stages: stage 1 - data input; stage 2 - preprocessing of input data (format check; size check; noise removal); stage 3 - object recognition using Convolutional Neural Networks (recognition of fire data; recognition of data on environmental factors; recognition of data on the nature of forest plantations); stage 4 - development of forest fire forecasting; stage 5 - output of the generated image with the operational forecast. To build and train artificial neural networks, a visual forest fire dynamics database was proposed to use. The developed forest fire forecasting models are based on a tree of artificial neural networks in the form of an acyclic graph and identify dependencies between the dynamics of a forest fire and the characteristics of the external and internal environment.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

Identifying the underlying factors associated with antidepressant drug discontinuation: Content analysis of patients’ drug reviews (Preprint)

2020 ◽  
Author(s):  
Mohammad Alarifi ◽  
Somaieh Goudarzvand3 ◽  
Abdulrahman Jabour ◽  
Doreen Foy ◽  
Maryam Zolnoori
Keyword(s):  
Machine Learning ◽  
Antidepressant Drug ◽  
Prediction Method ◽  
Analytical Framework ◽  
Structured Data ◽  
Withdrawal Symptoms ◽  
Machine Learning Techniques ◽  
Drug Discontinuation ◽  
Factors Associated ◽  
Learning Techniques

BACKGROUND The rate of antidepressant prescriptions is globally increasing. A large portion of patients stop their medications which could lead to many side effects including relapse, and anxiety. OBJECTIVE The aim of this was to develop a drug-continuity prediction model and identify the factors associated with drug-continuity using online patient forums. METHODS We retrieved 982 antidepressant drug reviews from the online patient’s forum AskaPatient.com. We followed the Analytical Framework Method to extract structured data from unstructured data. Using the structured data, we examined the factors associated with antidepressant discontinuity and developed a predictive model using multiple machine learning techniques. RESULTS We tested multiple machine learning techniques which resulted in different performances ranging from accuracy of 65% to 82%. We found that Radom Forest algorithm provides the highest prediction method with 82% Accuracy, 78% Precision, 88.03% Recall, and 84.2% F1-Score. The factors associated with drug discontinuity the most were; withdrawal symptoms, effectiveness-ineffectiveness, perceived-distress-adverse drug reaction, rating, and perceived-distress related to withdrawal symptoms. CONCLUSIONS Although the nature of data available at online forums differ from data collected through surveys, we found that online patients forum can be a valuable source of data for drug-continuity prediction and understanding patients experience. The factors identified through our techniques were consistent with the findings of prior studies that used surveys.

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